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The resolution and fi delity of global chronostratigraphic correlation are direct functions of the time period under consideration. By virtue of deep-ocean cores and astrochronology, the Cenozoic and Mesozoic time scales carry error bars of a few thousand years (k.y.) to a few hundred k.y. In contrast, most of the Paleozoic time scale carries error bars of plus or minus a few million years (m.y.), and chronostratigraphic control better than ±1 m.y. is considered "high resolution." The general lack of Paleozoic abyssal sediments and paucity of orbitally tuned Paleozoic data series combined with the relative incompleteness of the Paleozoic stratigraphic record have proven historically to be such an obstacle to intercontinental chronostratigraphic correlation that resolving the Paleozoic time scale to the level achieved during the Mesozoic and Cenozoic was viewed as impractical, impossible, or both.Here, we utilize integrated graptolite, conodont, and carbonate carbon isotope (δ 13 C carb ) data from three paleocontinents (Baltica, Avalonia, and Laurentia) to demonstrate chronostratigraphic control for upper Llando very through middle Wenlock (TelychianSheinwoodian , ~436-426 Ma) strata with a resolution of a few hundred k.y. The interval surrounding the base of the Wenlock Series can now be correlated globally with precision approaching 100 k.y., but some intervals (e.g., uppermost Telychian and upper Sheinwoodian) are either yet to be studied in sufficient detail or do not show suffi cient biologic speciation and/or extinction or carbon isotopic features to delineate such small time slices. Although producing such resolution during the Paleozoic presents an array of challenges unique to the era, we have begun to demonstrate that erecting a Paleozoic time scale comparable to that of younger eras is achievable.

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Accurate taxonomic classification of microfossils in thin-sections is an important biostratigraphic procedure. As paleontological expertise is typically restricted to specific taxonomic groups and experts are not present in all institutions, geoscience researchers often suffer from lack of quick access to critical taxonomic knowledge for biostratigraphic analyses. Moreover, diminishing emphasis on education and training in systematics poses a major challenge for the future of biostratigraphy, and on associated endeavors reliant on systematics. Here we present a machine learning approach to classify and organize fusulinids—microscopic index fossils for the late Paleozoic. The technique we employ has the potential to use such important taxonomic knowledge in models that can be applied to recognize and categorize fossil specimens. Our results demonstrate that, given adequate images and training, convolutional neural network models can correctly identify fusulinids with high levels of accuracy. Continued efforts in digitization of biological and paleontological collections at numerous museums and adoption of machine learning by paleontologists can enable the development of highly accurate and easy-to-use classification tools and, thus, facilitate biostratigraphic analyses by non-experts as well as allow for cross-validation of disparate collections around the world. Automation of classification work would also enable expert paleontologists and others to focus efforts on exploration of more complex interpretations and concepts.

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